4 citations · 9 across the 5 of their papers we have counts for
6 papers
Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles
Younkwan Lee, Jihyo Jeon, Jongmin Yu +1
Traffic scene recognition, which requires various visual classification tasks, is a critical ingredient in autonomous vehicles. However, most existing approaches treat each relevan…
Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform
Jongmin Yu, Duyong Kim, Younkwan Lee +1
In the past few years, the performance of road defect detection has been remarkably improved thanks to advancements on various studies on computer vision and deep learning. Althoug…
Unconstrained Road Marking Recognition with Generative Adversarial Networks
Younkwan Lee, Juhyun Lee, Yoojin Hong +2
Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning. Although considerable advances have been made, t…
Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution
Younkwan Lee, Jiwon Jun, Yoojin Hong +1
Although most current license plate (LP) recognition applications have been significantly advanced, they are still limited to ideal environments where training data are carefully a…
SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition
Younkwan Lee, Juhyun Lee, Hoyeon Ahn +1
In this paper, we present an algorithm for real-world license plate recognition (LPR) from a low-quality image. Our method is built upon a framework that includes denoising and rec…
Boosting Network Weight Separability via Feed-Backward Reconstruction
Jongmin Yu, Hyeontaek Oh
This paper proposes a new evaluation metric and boosting method for weight separability in neural network design. In contrast to general visual recognition methods designed to enco…